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pacman::p_load(jsonlite, tidygraph, ggraph, visNetwork, graphlayouts, ggforce,skimr,tidytext, tidyverse,igraph, topicmodels,tm)Uncover illegal, unreported, and unregulated (IUU) fishing activities through visual analytics
Using the data provide by the VAST challenge, we are looking into the Mini-Challenge 3 (MC3) to identify compaines possibly engaged in illegal, unreported, and unregulated (IUU) fishing.
pacman::p_load(jsonlite, tidygraph, ggraph, visNetwork, graphlayouts, ggforce,skimr,tidytext, tidyverse,igraph, topicmodels,tm)In the code chunk below, fromJSON() of jsonlite package is used to import MC3.json into R environment.
mc3_data <- fromJSON("data/MC3.json")Examine the data, this is not a directed graph, not looking into in- and out-degree of the nodes.
Below code chunk changes the links field into character field.
mc3_edges <- as_tibble(mc3_data$links)%>%
distinct() %>%
mutate(source = as.character(source),
target = as.character(target),
type = as.character(type)) %>%
group_by(source, target, type) %>%
summarise(weights = n()) %>%
filter(source!=target)%>%
ungroupmc3_nodes <- as_tibble(mc3_data$nodes) %>%
# distinct()%>%
mutate(country = as.character(country),
id = as.character(id),
product_services = as.character(product_services),
revenue_omu = as.numeric(as.character(revenue_omu)),
type = as.character(type)) %>%
select(id, country, type, revenue_omu, product_services)In the code chunk below, skim() of skimr package is used to display the summary statistics of mc3_edges tibble data frame.
skim(mc3_edges)| Name | mc3_edges |
| Number of rows | 24036 |
| Number of columns | 4 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| source | 0 | 1 | 6 | 700 | 0 | 12856 | 0 |
| target | 0 | 1 | 6 | 28 | 0 | 21265 | 0 |
| type | 0 | 1 | 16 | 16 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| weights | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | ▁▁▇▁▁ |
The report above reveals that there is not missing values in all fields.
In the code chunk below, datatable() of DT package is used to display mc3_edges tibble data frame as an interactive table on the html document.
DT::datatable(mc3_edges)Below code chunks, counting number of companies a person owns and the number of owners a company has.
ggplot(data = mc3_edges,
aes(x=type)) +
geom_bar()
unique_ids <- unique(mc3_edges$target)
num_unique_ids <- length(unique_ids)
num_unique_ids[1] 21265
Noofcompanies <- mc3_edges %>%
group_by(target, source, type) %>%
filter(type == "Beneficial Owner") %>%
summarise(count=n()) %>%
group_by(target)%>%
summarise(count=sum(count))
psych::describe(Noofcompanies) vars n mean sd median trimmed mad min max range skew
target* 1 15305 7653.0 4418.32 7653 7653 5672.43 1 15305 15304 0.00
count 2 15305 1.1 0.40 1 1 0.00 1 9 8 6.28
kurtosis se
target* -1.20 35.71
count 61.69 0.00
Noofowners <- mc3_edges %>%
group_by(source, target, type) %>%
summarise(count=n()) %>%
group_by(source)%>%
summarise(count=sum(count))
psych::describe(Noofowners) vars n mean sd median trimmed mad min max range skew
source* 1 12856 6428.50 3711.35 6428.5 6428.50 4765.08 1 12856 12855 0.00
count 2 12856 1.87 3.47 1.0 1.22 0.00 1 120 119 11.36
kurtosis se
source* -1.20 32.73
count 215.82 0.03
Below code chunk we are interested to see top 50 owners owning multiple companies, with John Smith and Michael Johnson have the highest of 9 companies to their name. This could be suspicious as why they need so many companies.
list_top_50 <- Noofcompanies %>%
arrange(desc(count)) %>%
top_n(50, wt = count)
ggplot(data = list_top_50,
aes(x = reorder(target, -count), y = count)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) 
skim(mc3_nodes)| Name | mc3_nodes |
| Number of rows | 27622 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| id | 0 | 1 | 6 | 64 | 0 | 22929 | 0 |
| country | 0 | 1 | 2 | 15 | 0 | 100 | 0 |
| type | 0 | 1 | 7 | 16 | 0 | 3 | 0 |
| product_services | 0 | 1 | 4 | 1737 | 0 | 3244 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| revenue_omu | 21515 | 0.22 | 1822155 | 18184433 | 3652.23 | 7676.36 | 16210.68 | 48327.66 | 310612303 | ▇▁▁▁▁ |
The report above reveals that there is no missing values in all fields.
In the code chunk below, datatable() of DT package is used to display mc3_nodes tibble data frame as an interactive table on the html document.
DT::datatable(mc3_nodes)Below code chunk to find out how is the distribution among the types of ownerhships.
ggplot(data = mc3_nodes,
aes(x = type)) +
geom_bar()
Below code chunk we check on the revenue distribution among the types of ownerships.
ggplot(data = mc3_nodes,
aes(x= type,
y = revenue_omu)) +
geom_boxplot()
We combined the nodes and edges data so we can find out more on the owner-company relationships.
combined <- left_join(mc3_nodes,mc3_edges,
by=c("id"="source"))Below code chunk to find out more on which owners have high number of companies also generating a lot of revenue.
combined <- combined %>%
group_by(target, type.y, id, country, type.x, product_services)%>%
summarize(revenue_omu) %>%
filter(type.y == "Beneficial Owner")
filtered_combined <- combined %>%
filter(target %in% list_top_50$target)%>%
arrange(desc(revenue_omu))
ggplot(data = filtered_combined,
aes(x = target, y = revenue_omu)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) 
Michael Johnson, Mark Miller and James Rodriguez stand out from the above chart, below code we want to see what business they did that generate more revenue.
Top_3_Revenue<- combined %>%
filter (target %in% c("Michael Johnson", "Mark Miller","James Rodriguez")) %>%
arrange(desc(revenue_omu))
DT::datatable(Top_3_Revenue)From the above data table, we see that Michael Johnson is involved in the fishing business and having many companies in different countries. The FishEye could probably look more into his business landscape across different companies and his business activities to understand more.
The code chunk below calculates number of times the word fish appeared in the field product_services.
mc3_nodes %>%
mutate(n_fish = str_count(product_services, "fish")) # A tibble: 27,622 × 6
id country type revenue_omu product_services n_fish
<chr> <chr> <chr> <dbl> <chr> <int>
1 Jones LLC ZH Comp… 310612303. Automobiles 0
2 Coleman, Hall and Lopez ZH Comp… 162734684. Passenger cars,… 0
3 Aqua Advancements Sashimi … Oceanus Comp… 115004667. Holding firm wh… 0
4 Makumba Ltd. Liability Co Utopor… Comp… 90986413. Car service, ca… 0
5 Taylor, Taylor and Farrell ZH Comp… 81466667. Fully electric … 0
6 Harmon, Edwards and Bates ZH Comp… 75070435. Discount superm… 0
7 Punjab s Marine conservati… Riodel… Comp… 72167572. Beef, pork, chi… 0
8 Assam Limited Liability … Utopor… Comp… 72162317. Power and Gas s… 0
9 Ianira Starfish Sagl Import Rio Is… Comp… 68832979. Light commercia… 0
10 Moran, Lewis and Jimenez ZH Comp… 65592906. Automobiles, tr… 0
# ℹ 27,612 more rows
The word tokenisation have different meaning in different scientific domains. In text sensing, tokenisation is the process of breaking up a given text into units called tokens. Tokens can be individual words, phrases or even whole sentences. In the process of tokenisation, some characters like punctuation marks may be discarded. The tokens usually become the input for the processes like parsing and text mining.
In the code chunk below, unnest_token() of tidytext is used to split text in product_services field into words.
token_nodes <- mc3_nodes %>%
unnest_tokens(word,
product_services)The two basic arguments to unnest_tokens() used here are column names. First we have the output column name that will be created as the text is unnested into it (word, in this case), and then the input column that the text comes from (product_services, in this case).
token_nodes %>%
count(word, sort = TRUE) %>%
top_n(15) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
geom_col() +
xlab(NULL) +
coord_flip() +
labs(x = "Count",
y = "Unique words",
title = "Count of unique words found in product_services field")
The bar chart reveals that the unique words contains some words that may not be useful to use. For instance “a” and “to”. In the word of text mining we call those words stop words. You want to remove these words from your analysis as they are fillers used to compose a sentence.
Using filter we also discover many “character(0)” which has no meaning in itself, we will also proceed to replace them with “NA”.
token_nodes$word[token_nodes$word == "character"] <- "NA"
token_nodes$word[token_nodes$word == "0"] <- "NA"stopwords_removed <- token_nodes %>%
anti_join(stop_words)stopwords_removed %>%
count(word, sort = TRUE) %>%
top_n(15) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
geom_col() +
xlab(NULL) +
coord_flip() +
labs(x = "Count",
y = "Unique words",
title = "Count of unique words found in product_services field")
stopwords_removed %>%
count(word, sort = TRUE) %>%
top_n(20) %>%
mutate(word = reorder(word, n)) %>%
filter(!word %in% head(word, 3)) %>%
ggplot(aes(x = word, y = n)) +
geom_col() +
xlab(NULL) +
coord_flip() +
labs(x = "Count",
y = "Unique words",
title = "Count of unique words found in product_services field")
From the above text insights, we are interested to see the network of companies of Beneficial Owners with fish as their product services.
mc3_nodes_fish <- stopwords_removed %>%
filter(stopwords_removed$word == "fish")mc3_edges_fish <- mc3_edges[mc3_edges$source %in% mc3_nodes_fish$id,] %>%
filter(type == "Beneficial Owner")
id1 <- mc3_edges_fish %>%
select(source) %>%
rename(id = source)
id2 <- mc3_edges_fish %>%
select(target) %>%
rename(id = target)
mc3_nodes_fish <- rbind(id1, id2) %>%
distinct() %>%
left_join(mc3_nodes_fish,
unmatched = "drop") mc3_graph <- tbl_graph(nodes = mc3_nodes_fish,
edges = mc3_edges_fish,
directed = FALSE)
mc3_graph<-mc3_graph%>%
mutate(betweenness=centrality_betweenness())
mc3_graph# A tbl_graph: 1190 nodes and 876 edges
#
# An unrooted forest with 314 trees
#
# A tibble: 1,190 × 6
id country type revenue_omu word betweenness
<chr> <chr> <chr> <dbl> <chr> <dbl>
1 Adams Group ZH Comp… 9056. fish 0
2 Albertine Rift NV Family Marebak Comp… 9761. fish 3
3 Allen PLC ZH Comp… 61582. fish 0
4 Ancla del Mar Pic Worldwide Thessa… Comp… 4667. fish 3
5 Andhra Pradesh OJSC Marine conse… Yggdra… Comp… 25758. fish 0
6 Andhra Pradesh OJSC Marine conse… Yggdra… Comp… 25758. fish 0
# ℹ 1,184 more rows
#
# A tibble: 876 × 4
from to type weights
<int> <int> <chr> <int>
1 1 321 Beneficial Owner 1
2 2 322 Beneficial Owner 1
3 2 323 Beneficial Owner 1
# ℹ 873 more rows
Using the distribution function to understand the centrality_betweenness().
ggplot(as.data.frame(mc3_graph),aes(x=betweenness))+
geom_histogram(bins=10,fill="lightblue",colour="black")+
ggtitle("Distribution of centrality betweenness")+
theme(plot.title = element_text(hjust=0.5))
Looking at this, we can filter our records where the centrality between is greater than 50 to understand the interactions.
set.seed (1234)
degrees <- degree(mc3_graph)
V(mc3_graph)$degree <- degrees
mc3_graph %>%
filter(betweenness >= 50) %>%
ggraph(layout = "fr") +
geom_edge_link(aes(alpha=0.5)) +
geom_node_point(aes(
size = betweenness,
colors = "lightblue",
alpha = 0.5)) +
scale_size_continuous(range=c(1,10))+
geom_node_text(aes(label = id, filter= betweenness >=50 & degree >0), repel = TRUE)+
theme_graph()
list_top_30 <- Noofowners %>%
arrange(desc(count)) %>%
top_n(30, wt = count)
ggplot(data = list_top_30,
aes(x = reorder(source, -count), y = count)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) 
corpus <- Corpus(VectorSource(stopwords_removed$word))
# Convert the corpus to a document-term matrix
dtm <- DocumentTermMatrix(corpus)
# Convert the document-term matrix to a tidy format
tidy_dtm <- tidy(dtm)
tidy_dtm# A tibble: 50,357 × 3
document term count
<chr> <chr> <dbl>
1 1 automobiles 1
2 2 passenger 1
3 3 cars 1
4 4 trucks 1
5 5 vans 1
6 6 buses 1
7 7 holding 1
8 8 firm 1
9 9 subsidiaries 1
10 10 engaged 1
# ℹ 50,347 more rows
# vocabulary <- tidy_dtm %>%
# filter(count > 1)
#
# str(tidy_dtm)
# head(tidy_dtm)
#
# # Create the LDA model
# lda_model <- LDA(tidy_dtm, k = 5, control = list(seed = 1234)) # topics <- tidy(lda_model, matrix = "beta")
#
# topics